Overview

Dataset statistics

Number of variables22
Number of observations25000
Missing cells1898
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 MiB
Average record size in memory176.0 B

Variable types

Categorical10
Numeric12

Alerts

num_refill_req_l3m is highly overall correlated with demand_interactionHigh correlation
storage_issue_reported_l3m is highly overall correlated with product_wg_tonHigh correlation
govt_check_l3m is highly overall correlated with WH_capacity_size and 1 other fieldsHigh correlation
product_wg_ton is highly overall correlated with storage_issue_reported_l3mHigh correlation
demand_interaction is highly overall correlated with num_refill_req_l3mHigh correlation
WH_capacity_size is highly overall correlated with govt_check_l3m and 1 other fieldsHigh correlation
WH_regional_zone is highly overall correlated with govt_check_l3m and 1 other fieldsHigh correlation
Location_type is highly imbalanced (59.2%)Imbalance
flood_impacted is highly imbalanced (53.7%)Imbalance
flood_proof is highly imbalanced (69.4%)Imbalance
workers_num has 990 (4.0%) missing valuesMissing
approved_wh_govt_certificate has 908 (3.6%) missing valuesMissing
num_refill_req_l3m has 2912 (11.6%) zerosZeros
transport_issue_l1y has 15215 (60.9%) zerosZeros
storage_issue_reported_l3m has 908 (3.6%) zerosZeros
wh_breakdown_l3m has 908 (3.6%) zerosZeros
demand_interaction has 2912 (11.6%) zerosZeros

Reproduction

Analysis started2023-12-08 05:41:17.905763
Analysis finished2023-12-08 05:41:37.082182
Duration19.18 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Location_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
Rural
22957 
Urban
 
2043

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters125000
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowRural
3rd rowRural
4th rowRural
5th rowRural

Common Values

ValueCountFrequency (%)
Rural 22957
91.8%
Urban 2043
 
8.2%

Length

2023-12-08T11:11:37.177450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-08T11:11:37.300318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
rural 22957
91.8%
urban 2043
 
8.2%

Most occurring characters

ValueCountFrequency (%)
r 25000
20.0%
a 25000
20.0%
R 22957
18.4%
u 22957
18.4%
l 22957
18.4%
U 2043
 
1.6%
b 2043
 
1.6%
n 2043
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100000
80.0%
Uppercase Letter 25000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 25000
25.0%
a 25000
25.0%
u 22957
23.0%
l 22957
23.0%
b 2043
 
2.0%
n 2043
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
R 22957
91.8%
U 2043
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 125000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 25000
20.0%
a 25000
20.0%
R 22957
18.4%
u 22957
18.4%
l 22957
18.4%
U 2043
 
1.6%
b 2043
 
1.6%
n 2043
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 25000
20.0%
a 25000
20.0%
R 22957
18.4%
u 22957
18.4%
l 22957
18.4%
U 2043
 
1.6%
b 2043
 
1.6%
n 2043
 
1.6%

WH_capacity_size
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
Large
10169 
Mid
10020 
Small
4811 

Length

Max length5
Median length5
Mean length4.1984
Min length3

Characters and Unicode

Total characters104960
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmall
2nd rowLarge
3rd rowMid
4th rowMid
5th rowLarge

Common Values

ValueCountFrequency (%)
Large 10169
40.7%
Mid 10020
40.1%
Small 4811
19.2%

Length

2023-12-08T11:11:37.382908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-08T11:11:37.486883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
large 10169
40.7%
mid 10020
40.1%
small 4811
19.2%

Most occurring characters

ValueCountFrequency (%)
a 14980
14.3%
L 10169
9.7%
r 10169
9.7%
g 10169
9.7%
e 10169
9.7%
M 10020
9.5%
i 10020
9.5%
d 10020
9.5%
l 9622
9.2%
S 4811
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79960
76.2%
Uppercase Letter 25000
 
23.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14980
18.7%
r 10169
12.7%
g 10169
12.7%
e 10169
12.7%
i 10020
12.5%
d 10020
12.5%
l 9622
12.0%
m 4811
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
L 10169
40.7%
M 10020
40.1%
S 4811
19.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 104960
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14980
14.3%
L 10169
9.7%
r 10169
9.7%
g 10169
9.7%
e 10169
9.7%
M 10020
9.5%
i 10020
9.5%
d 10020
9.5%
l 9622
9.2%
S 4811
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14980
14.3%
L 10169
9.7%
r 10169
9.7%
g 10169
9.7%
e 10169
9.7%
M 10020
9.5%
i 10020
9.5%
d 10020
9.5%
l 9622
9.2%
S 4811
 
4.6%

zone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
North
10278 
West
7931 
South
6362 
East
 
429

Length

Max length5
Median length5
Mean length4.6656
Min length4

Characters and Unicode

Total characters116640
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowNorth
3rd rowSouth
4th rowNorth
5th rowNorth

Common Values

ValueCountFrequency (%)
North 10278
41.1%
West 7931
31.7%
South 6362
25.4%
East 429
 
1.7%

Length

2023-12-08T11:11:37.597909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-08T11:11:37.685538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
north 10278
41.1%
west 7931
31.7%
south 6362
25.4%
east 429
 
1.7%

Most occurring characters

ValueCountFrequency (%)
t 25000
21.4%
o 16640
14.3%
h 16640
14.3%
N 10278
8.8%
r 10278
8.8%
s 8360
 
7.2%
W 7931
 
6.8%
e 7931
 
6.8%
S 6362
 
5.5%
u 6362
 
5.5%
Other values (2) 858
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91640
78.6%
Uppercase Letter 25000
 
21.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 25000
27.3%
o 16640
18.2%
h 16640
18.2%
r 10278
11.2%
s 8360
 
9.1%
e 7931
 
8.7%
u 6362
 
6.9%
a 429
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 10278
41.1%
W 7931
31.7%
S 6362
25.4%
E 429
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 116640
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 25000
21.4%
o 16640
14.3%
h 16640
14.3%
N 10278
8.8%
r 10278
8.8%
s 8360
 
7.2%
W 7931
 
6.8%
e 7931
 
6.8%
S 6362
 
5.5%
u 6362
 
5.5%
Other values (2) 858
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 25000
21.4%
o 16640
14.3%
h 16640
14.3%
N 10278
8.8%
r 10278
8.8%
s 8360
 
7.2%
W 7931
 
6.8%
e 7931
 
6.8%
S 6362
 
5.5%
u 6362
 
5.5%
Other values (2) 858
 
0.7%

WH_regional_zone
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
Zone 6
8339 
Zone 5
4587 
Zone 4
4176 
Zone 2
2963 
Zone 3
2881 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters150000
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZone 6
2nd rowZone 5
3rd rowZone 2
4th rowZone 3
5th rowZone 5

Common Values

ValueCountFrequency (%)
Zone 6 8339
33.4%
Zone 5 4587
18.3%
Zone 4 4176
16.7%
Zone 2 2963
 
11.9%
Zone 3 2881
 
11.5%
Zone 1 2054
 
8.2%

Length

2023-12-08T11:11:37.787483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-08T11:11:37.928010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
zone 25000
50.0%
6 8339
 
16.7%
5 4587
 
9.2%
4 4176
 
8.4%
2 2963
 
5.9%
3 2881
 
5.8%
1 2054
 
4.1%

Most occurring characters

ValueCountFrequency (%)
Z 25000
16.7%
o 25000
16.7%
n 25000
16.7%
e 25000
16.7%
25000
16.7%
6 8339
 
5.6%
5 4587
 
3.1%
4 4176
 
2.8%
2 2963
 
2.0%
3 2881
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75000
50.0%
Uppercase Letter 25000
 
16.7%
Space Separator 25000
 
16.7%
Decimal Number 25000
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 8339
33.4%
5 4587
18.3%
4 4176
16.7%
2 2963
 
11.9%
3 2881
 
11.5%
1 2054
 
8.2%
Lowercase Letter
ValueCountFrequency (%)
o 25000
33.3%
n 25000
33.3%
e 25000
33.3%
Uppercase Letter
ValueCountFrequency (%)
Z 25000
100.0%
Space Separator
ValueCountFrequency (%)
25000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100000
66.7%
Common 50000
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
25000
50.0%
6 8339
 
16.7%
5 4587
 
9.2%
4 4176
 
8.4%
2 2963
 
5.9%
3 2881
 
5.8%
1 2054
 
4.1%
Latin
ValueCountFrequency (%)
Z 25000
25.0%
o 25000
25.0%
n 25000
25.0%
e 25000
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Z 25000
16.7%
o 25000
16.7%
n 25000
16.7%
e 25000
16.7%
25000
16.7%
6 8339
 
5.6%
5 4587
 
3.1%
4 4176
 
2.8%
2 2963
 
2.0%
3 2881
 
1.9%

num_refill_req_l3m
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.08904
Minimum0
Maximum8
Zeros2912
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-12-08T11:11:38.057243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6066115
Coefficient of variation (CV)0.63746296
Kurtosis-1.2206972
Mean4.08904
Median Absolute Deviation (MAD)2
Skewness-0.075216703
Sum102226
Variance6.7944237
MonotonicityNot monotonic
2023-12-08T11:11:38.146210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 2990
12.0%
8 2970
11.9%
5 2941
11.8%
0 2912
11.6%
7 2873
11.5%
1 2856
11.4%
4 2846
11.4%
6 2804
11.2%
2 1808
7.2%
ValueCountFrequency (%)
0 2912
11.6%
1 2856
11.4%
2 1808
7.2%
3 2990
12.0%
4 2846
11.4%
5 2941
11.8%
6 2804
11.2%
7 2873
11.5%
8 2970
11.9%
ValueCountFrequency (%)
8 2970
11.9%
7 2873
11.5%
6 2804
11.2%
5 2941
11.8%
4 2846
11.4%
3 2990
12.0%
2 1808
7.2%
1 2856
11.4%
0 2912
11.6%

transport_issue_l1y
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77368
Minimum0
Maximum5
Zeros15215
Zeros (%)60.9%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-12-08T11:11:38.235402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1994485
Coefficient of variation (CV)1.5503161
Kurtosis1.8384391
Mean0.77368
Median Absolute Deviation (MAD)0
Skewness1.6109066
Sum19342
Variance1.4386768
MonotonicityNot monotonic
2023-12-08T11:11:38.326019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 15215
60.9%
1 4644
 
18.6%
2 2198
 
8.8%
3 1818
 
7.3%
4 777
 
3.1%
5 348
 
1.4%
ValueCountFrequency (%)
0 15215
60.9%
1 4644
 
18.6%
2 2198
 
8.8%
3 1818
 
7.3%
4 777
 
3.1%
5 348
 
1.4%
ValueCountFrequency (%)
5 348
 
1.4%
4 777
 
3.1%
3 1818
 
7.3%
2 2198
 
8.8%
1 4644
 
18.6%
0 15215
60.9%

Competitor_in_mkt
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1042
Minimum0
Maximum12
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-12-08T11:11:38.421150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.141663
Coefficient of variation (CV)0.36778012
Kurtosis1.7863684
Mean3.1042
Median Absolute Deviation (MAD)1
Skewness0.97845565
Sum77605
Variance1.3033945
MonotonicityNot monotonic
2023-12-08T11:11:38.514060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 8669
34.7%
3 7094
28.4%
4 6708
26.8%
5 1265
 
5.1%
6 546
 
2.2%
1 432
 
1.7%
7 189
 
0.8%
8 76
 
0.3%
9 13
 
0.1%
10 6
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 432
 
1.7%
2 8669
34.7%
3 7094
28.4%
4 6708
26.8%
5 1265
 
5.1%
6 546
 
2.2%
7 189
 
0.8%
8 76
 
0.3%
9 13
 
0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 6
 
< 0.1%
9 13
 
0.1%
8 76
 
0.3%
7 189
 
0.8%
6 546
 
2.2%
5 1265
 
5.1%
4 6708
26.8%
3 7094
28.4%
2 8669
34.7%

retail_shop_num
Real number (ℝ)

Distinct4906
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4985.7116
Minimum1821
Maximum11008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-12-08T11:11:38.634471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1821
5-th percentile3513
Q14313
median4859
Q35500
95-th percentile6934.1
Maximum11008
Range9187
Interquartile range (IQR)1187

Descriptive statistics

Standard deviation1052.8253
Coefficient of variation (CV)0.2111685
Kurtosis1.851946
Mean4985.7116
Median Absolute Deviation (MAD)587
Skewness0.90830174
Sum1.2464279 × 108
Variance1108441
MonotonicityNot monotonic
2023-12-08T11:11:38.792862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4860 22
 
0.1%
4808 22
 
0.1%
4367 21
 
0.1%
4816 21
 
0.1%
4659 21
 
0.1%
4611 21
 
0.1%
5022 21
 
0.1%
4918 20
 
0.1%
4783 20
 
0.1%
4850 20
 
0.1%
Other values (4896) 24791
99.2%
ValueCountFrequency (%)
1821 1
< 0.1%
1871 1
< 0.1%
1905 1
< 0.1%
1915 1
< 0.1%
1953 1
< 0.1%
1959 1
< 0.1%
1971 1
< 0.1%
1980 1
< 0.1%
1999 1
< 0.1%
2008 1
< 0.1%
ValueCountFrequency (%)
11008 1
< 0.1%
10846 1
< 0.1%
10562 1
< 0.1%
10320 1
< 0.1%
10224 1
< 0.1%
10169 1
< 0.1%
10156 1
< 0.1%
10151 1
< 0.1%
10150 1
< 0.1%
10041 1
< 0.1%

wh_owner_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
Company Owned
13578 
Rented
11422 

Length

Max length13
Median length13
Mean length9.80184
Min length6

Characters and Unicode

Total characters245046
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRented
2nd rowCompany Owned
3rd rowCompany Owned
4th rowRented
5th rowCompany Owned

Common Values

ValueCountFrequency (%)
Company Owned 13578
54.3%
Rented 11422
45.7%

Length

2023-12-08T11:11:38.913228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-08T11:11:39.059497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
company 13578
35.2%
owned 13578
35.2%
rented 11422
29.6%

Most occurring characters

ValueCountFrequency (%)
n 38578
15.7%
e 36422
14.9%
d 25000
10.2%
C 13578
 
5.5%
o 13578
 
5.5%
m 13578
 
5.5%
p 13578
 
5.5%
a 13578
 
5.5%
y 13578
 
5.5%
13578
 
5.5%
Other values (4) 50000
20.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 192890
78.7%
Uppercase Letter 38578
 
15.7%
Space Separator 13578
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 38578
20.0%
e 36422
18.9%
d 25000
13.0%
o 13578
 
7.0%
m 13578
 
7.0%
p 13578
 
7.0%
a 13578
 
7.0%
y 13578
 
7.0%
w 13578
 
7.0%
t 11422
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
C 13578
35.2%
O 13578
35.2%
R 11422
29.6%
Space Separator
ValueCountFrequency (%)
13578
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 231468
94.5%
Common 13578
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 38578
16.7%
e 36422
15.7%
d 25000
10.8%
C 13578
 
5.9%
o 13578
 
5.9%
m 13578
 
5.9%
p 13578
 
5.9%
a 13578
 
5.9%
y 13578
 
5.9%
O 13578
 
5.9%
Other values (3) 36422
15.7%
Common
ValueCountFrequency (%)
13578
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 245046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 38578
15.7%
e 36422
14.9%
d 25000
10.2%
C 13578
 
5.5%
o 13578
 
5.5%
m 13578
 
5.5%
p 13578
 
5.5%
a 13578
 
5.5%
y 13578
 
5.5%
13578
 
5.5%
Other values (4) 50000
20.4%

distributor_num
Real number (ℝ)

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.41812
Minimum15
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-12-08T11:11:39.211915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile17
Q129
median42
Q356
95-th percentile68
Maximum70
Range55
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.064329
Coefficient of variation (CV)0.37871383
Kurtosis-1.1875636
Mean42.41812
Median Absolute Deviation (MAD)14
Skewness0.015212662
Sum1060453
Variance258.06266
MonotonicityNot monotonic
2023-12-08T11:11:39.323643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 490
 
2.0%
41 481
 
1.9%
69 481
 
1.9%
37 479
 
1.9%
29 479
 
1.9%
21 478
 
1.9%
40 477
 
1.9%
28 474
 
1.9%
47 469
 
1.9%
33 467
 
1.9%
Other values (46) 20225
80.9%
ValueCountFrequency (%)
15 436
1.7%
16 431
1.7%
17 415
1.7%
18 439
1.8%
19 416
1.7%
20 440
1.8%
21 478
1.9%
22 460
1.8%
23 450
1.8%
24 454
1.8%
ValueCountFrequency (%)
70 438
1.8%
69 481
1.9%
68 400
1.6%
67 422
1.7%
66 421
1.7%
65 453
1.8%
64 452
1.8%
63 459
1.8%
62 447
1.8%
61 444
1.8%

flood_impacted
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
0
22546 
1
2454 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 22546
90.2%
1 2454
 
9.8%

Length

2023-12-08T11:11:39.431716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-08T11:11:39.517408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22546
90.2%
1 2454
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 22546
90.2%
1 2454
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22546
90.2%
1 2454
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 25000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22546
90.2%
1 2454
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22546
90.2%
1 2454
 
9.8%

flood_proof
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
0
23634 
1
 
1366

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23634
94.5%
1 1366
 
5.5%

Length

2023-12-08T11:11:39.587482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-08T11:11:39.666152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23634
94.5%
1 1366
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 23634
94.5%
1 1366
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23634
94.5%
1 1366
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 25000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23634
94.5%
1 1366
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23634
94.5%
1 1366
 
5.5%

electric_supply
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
1
16422 
0
8578 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 16422
65.7%
0 8578
34.3%

Length

2023-12-08T11:11:39.733727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-08T11:11:39.813584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 16422
65.7%
0 8578
34.3%

Most occurring characters

ValueCountFrequency (%)
1 16422
65.7%
0 8578
34.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16422
65.7%
0 8578
34.3%

Most occurring scripts

ValueCountFrequency (%)
Common 25000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16422
65.7%
0 8578
34.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16422
65.7%
0 8578
34.3%

dist_from_hub
Real number (ℝ)

Distinct217
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.53732
Minimum55
Maximum271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-12-08T11:11:39.899109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile65
Q1109
median164
Q3218
95-th percentile261
Maximum271
Range216
Interquartile range (IQR)109

Descriptive statistics

Standard deviation62.718609
Coefficient of variation (CV)0.38351252
Kurtosis-1.2006823
Mean163.53732
Median Absolute Deviation (MAD)54
Skewness-0.005998691
Sum4088433
Variance3933.624
MonotonicityNot monotonic
2023-12-08T11:11:40.001644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239 144
 
0.6%
84 142
 
0.6%
209 140
 
0.6%
258 140
 
0.6%
204 140
 
0.6%
242 138
 
0.6%
145 138
 
0.6%
256 137
 
0.5%
186 136
 
0.5%
108 135
 
0.5%
Other values (207) 23610
94.4%
ValueCountFrequency (%)
55 104
0.4%
56 111
0.4%
57 126
0.5%
58 122
0.5%
59 113
0.5%
60 107
0.4%
61 112
0.4%
62 118
0.5%
63 128
0.5%
64 100
0.4%
ValueCountFrequency (%)
271 130
0.5%
270 129
0.5%
269 125
0.5%
268 121
0.5%
267 123
0.5%
266 103
0.4%
265 116
0.5%
264 103
0.4%
263 103
0.4%
262 107
0.4%

workers_num
Real number (ℝ)

Distinct60
Distinct (%)0.2%
Missing990
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean28.944398
Minimum10
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-12-08T11:11:40.132272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile18
Q124
median28
Q333
95-th percentile43
Maximum98
Range88
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.872534
Coefficient of variation (CV)0.27198817
Kurtosis3.4093352
Mean28.944398
Median Absolute Deviation (MAD)5
Skewness1.0599106
Sum694955
Variance61.976791
MonotonicityNot monotonic
2023-12-08T11:11:40.244480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 1491
 
6.0%
27 1447
 
5.8%
29 1402
 
5.6%
26 1290
 
5.2%
25 1268
 
5.1%
24 1228
 
4.9%
30 1202
 
4.8%
31 1132
 
4.5%
23 1077
 
4.3%
32 1077
 
4.3%
Other values (50) 11396
45.6%
(Missing) 990
 
4.0%
ValueCountFrequency (%)
10 5
 
< 0.1%
11 5
 
< 0.1%
12 15
 
0.1%
13 24
 
0.1%
14 104
 
0.4%
15 155
 
0.6%
16 328
1.3%
17 445
1.8%
18 559
2.2%
19 590
2.4%
ValueCountFrequency (%)
98 5
 
< 0.1%
92 5
 
< 0.1%
78 5
 
< 0.1%
72 5
 
< 0.1%
67 5
 
< 0.1%
65 5
 
< 0.1%
64 5
 
< 0.1%
63 5
 
< 0.1%
62 5
 
< 0.1%
61 14
0.1%

storage_issue_reported_l3m
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.13044
Minimum0
Maximum39
Zeros908
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-12-08T11:11:40.371509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q110
median18
Q324
95-th percentile33
Maximum39
Range39
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.1611081
Coefficient of variation (CV)0.53478534
Kurtosis-0.6801423
Mean17.13044
Median Absolute Deviation (MAD)7
Skewness0.11334521
Sum428261
Variance83.925902
MonotonicityNot monotonic
2023-12-08T11:11:40.924319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
24 1424
 
5.7%
5 1351
 
5.4%
25 1262
 
5.0%
4 1081
 
4.3%
18 1070
 
4.3%
20 1065
 
4.3%
6 1056
 
4.2%
19 1022
 
4.1%
16 938
 
3.8%
23 917
 
3.7%
Other values (27) 13814
55.3%
ValueCountFrequency (%)
0 908
3.6%
4 1081
4.3%
5 1351
5.4%
6 1056
4.2%
7 491
 
2.0%
8 406
 
1.6%
9 787
3.1%
10 637
2.5%
11 867
3.5%
12 739
3.0%
ValueCountFrequency (%)
39 156
0.6%
38 181
0.7%
37 141
0.6%
36 161
0.6%
35 181
0.7%
34 288
1.2%
33 295
1.2%
32 296
1.2%
31 289
1.2%
30 337
1.3%

temp_reg_mach
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size195.4 KiB
0
17418 
1
7582 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 17418
69.7%
1 7582
30.3%

Length

2023-12-08T11:11:41.039546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-08T11:11:41.122345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17418
69.7%
1 7582
30.3%

Most occurring characters

ValueCountFrequency (%)
0 17418
69.7%
1 7582
30.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17418
69.7%
1 7582
30.3%

Most occurring scripts

ValueCountFrequency (%)
Common 25000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17418
69.7%
1 7582
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17418
69.7%
1 7582
30.3%
Distinct5
Distinct (%)< 0.1%
Missing908
Missing (%)3.6%
Memory size195.4 KiB
C
5501 
B+
4917 
B
4812 
A
4671 
A+
4191 

Length

Max length2
Median length1
Mean length1.3780508
Min length1

Characters and Unicode

Total characters33200
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA+
5th rowC

Common Values

ValueCountFrequency (%)
C 5501
22.0%
B+ 4917
19.7%
B 4812
19.2%
A 4671
18.7%
A+ 4191
16.8%
(Missing) 908
 
3.6%

Length

2023-12-08T11:11:41.204582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-08T11:11:41.311455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
b 9729
40.4%
a 8862
36.8%
c 5501
22.8%

Most occurring characters

ValueCountFrequency (%)
B 9729
29.3%
+ 9108
27.4%
A 8862
26.7%
C 5501
16.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24092
72.6%
Math Symbol 9108
 
27.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 9729
40.4%
A 8862
36.8%
C 5501
22.8%
Math Symbol
ValueCountFrequency (%)
+ 9108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24092
72.6%
Common 9108
 
27.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 9729
40.4%
A 8862
36.8%
C 5501
22.8%
Common
ValueCountFrequency (%)
+ 9108
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 9729
29.3%
+ 9108
27.4%
A 8862
26.7%
C 5501
16.6%

wh_breakdown_l3m
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.48204
Minimum0
Maximum6
Zeros908
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-12-08T11:11:41.395814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6903348
Coefficient of variation (CV)0.48544382
Kurtosis-0.95214878
Mean3.48204
Median Absolute Deviation (MAD)1
Skewness-0.06802568
Sum87051
Variance2.8572317
MonotonicityNot monotonic
2023-12-08T11:11:41.467978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 5076
20.3%
3 5006
20.0%
4 4037
16.1%
6 4012
16.0%
5 3925
15.7%
1 2036
8.1%
0 908
 
3.6%
ValueCountFrequency (%)
0 908
 
3.6%
1 2036
8.1%
2 5076
20.3%
3 5006
20.0%
4 4037
16.1%
5 3925
15.7%
6 4012
16.0%
ValueCountFrequency (%)
6 4012
16.0%
5 3925
15.7%
4 4037
16.1%
3 5006
20.0%
2 5076
20.3%
1 2036
8.1%
0 908
 
3.6%

govt_check_l3m
Real number (ℝ)

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.81228
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-12-08T11:11:41.572941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q326
95-th percentile31
Maximum32
Range31
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6323822
Coefficient of variation (CV)0.45886953
Kurtosis-1.0573419
Mean18.81228
Median Absolute Deviation (MAD)7
Skewness-0.36326153
Sum470307
Variance74.518022
MonotonicityNot monotonic
2023-12-08T11:11:41.693810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
26 2908
 
11.6%
23 1828
 
7.3%
19 1604
 
6.4%
28 1465
 
5.9%
14 1429
 
5.7%
27 1277
 
5.1%
6 1224
 
4.9%
11 1160
 
4.6%
12 947
 
3.8%
32 940
 
3.8%
Other values (22) 10218
40.9%
ValueCountFrequency (%)
1 550
2.2%
2 431
 
1.7%
3 438
 
1.8%
4 99
 
0.4%
5 250
 
1.0%
6 1224
4.9%
7 65
 
0.3%
8 276
 
1.1%
9 932
3.7%
10 899
3.6%
ValueCountFrequency (%)
32 940
 
3.8%
31 362
 
1.4%
30 404
 
1.6%
29 901
 
3.6%
28 1465
5.9%
27 1277
5.1%
26 2908
11.6%
25 884
 
3.5%
24 628
 
2.5%
23 1828
7.3%

product_wg_ton
Real number (ℝ)

Distinct4561
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22102.633
Minimum2065
Maximum55151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-12-08T11:11:41.817911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2065
5-th percentile5128
Q113059
median22101
Q330103
95-th percentile43113
Maximum55151
Range53086
Interquartile range (IQR)17044

Descriptive statistics

Standard deviation11607.755
Coefficient of variation (CV)0.52517522
Kurtosis-0.5020222
Mean22102.633
Median Absolute Deviation (MAD)8959
Skewness0.33163104
Sum5.5256582 × 108
Variance1.3473998 × 108
MonotonicityNot monotonic
2023-12-08T11:11:41.930060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6081 21
 
0.1%
5146 21
 
0.1%
6057 21
 
0.1%
6104 20
 
0.1%
6099 20
 
0.1%
6083 20
 
0.1%
6107 19
 
0.1%
6066 19
 
0.1%
31126 19
 
0.1%
6097 19
 
0.1%
Other values (4551) 24801
99.2%
ValueCountFrequency (%)
2065 1
< 0.1%
2083 1
< 0.1%
2093 1
< 0.1%
2103 1
< 0.1%
2104 1
< 0.1%
2106 1
< 0.1%
2109 1
< 0.1%
2118 1
< 0.1%
2122 1
< 0.1%
2133 1
< 0.1%
ValueCountFrequency (%)
55151 1
< 0.1%
55150 1
< 0.1%
55144 1
< 0.1%
55132 1
< 0.1%
55120 1
< 0.1%
55115 1
< 0.1%
55112 1
< 0.1%
55111 1
< 0.1%
55095 1
< 0.1%
55093 1
< 0.1%

demand_interaction
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13522
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20383.519
Minimum0
Maximum88064
Zeros2912
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2023-12-08T11:11:42.049196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18656
median19924.5
Q330680
95-th percentile43160.4
Maximum88064
Range88064
Interquartile range (IQR)22024

Descriptive statistics

Standard deviation13933.705
Coefficient of variation (CV)0.68357702
Kurtosis-0.46351099
Mean20383.519
Median Absolute Deviation (MAD)10974.5
Skewness0.32287844
Sum5.0958796 × 108
Variance1.9414813 × 108
MonotonicityNot monotonic
2023-12-08T11:11:42.160840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2912
 
11.6%
21840 9
 
< 0.1%
29460 9
 
< 0.1%
35392 8
 
< 0.1%
30240 8
 
< 0.1%
39536 8
 
< 0.1%
30072 8
 
< 0.1%
30702 8
 
< 0.1%
20024 8
 
< 0.1%
22180 8
 
< 0.1%
Other values (13512) 22014
88.1%
ValueCountFrequency (%)
0 2912
11.6%
2044 1
 
< 0.1%
2224 1
 
< 0.1%
2326 1
 
< 0.1%
2385 1
 
< 0.1%
2443 1
 
< 0.1%
2445 1
 
< 0.1%
2497 1
 
< 0.1%
2508 1
 
< 0.1%
2520 1
 
< 0.1%
ValueCountFrequency (%)
88064 1
< 0.1%
80288 1
< 0.1%
77552 1
< 0.1%
76648 1
< 0.1%
76472 1
< 0.1%
75280 1
< 0.1%
73968 1
< 0.1%
73912 1
< 0.1%
73896 1
< 0.1%
73880 1
< 0.1%

Interactions

2023-12-08T11:11:35.102424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:20.438743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:21.737709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:23.241357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:24.468358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:25.700393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:26.943070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:28.355728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:29.535684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:30.929467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:32.222339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:33.826989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:35.219834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:20.555307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:21.841896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:23.345891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:24.578291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:25.799580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:27.053070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:28.456261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:29.648037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:31.054365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:32.324543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:33.932719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:35.310549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:20.660789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:21.939006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:23.442639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:24.676022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:25.895741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:27.145490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:28.551822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:29.763994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:31.154656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:32.443166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:34.035718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:35.409950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:20.771038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:22.052332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:23.551543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:24.781349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:25.995421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:27.240847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:28.648672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:29.873735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:31.254015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:32.569736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:34.155946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:35.507040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:20.874572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:22.156177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:23.649481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:24.876336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:26.091812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:27.566539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:28.757490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:29.993116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:31.353885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:32.667765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:34.261919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:35.604001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:20.978314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:22.253415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:23.757772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:24.987162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:26.191891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:27.662926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:28.856123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:30.121184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:31.464063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:32.778220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:34.361985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:35.697501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:21.087585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:22.578999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:23.848281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:25.081421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:26.289423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:27.766513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:28.951652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:30.240479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:31.577610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:32.876093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:34.465739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:35.805014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:21.186047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:22.670825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:23.941832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:25.171762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:26.383240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:27.863894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:29.048601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:30.364249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:31.679871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:32.973543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:34.580053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:35.912385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:21.298781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:22.821837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:24.065601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:25.275239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:26.492940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:27.967954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:29.153547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:30.496710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:31.809085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:33.087319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:34.689618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:36.007437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:21.420001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:22.942839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:24.160206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:25.383475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:26.629099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:28.069074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:29.255098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:30.600828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:31.924413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:33.208829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:34.806627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:36.105489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:21.530308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:23.049782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:24.260870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:25.492598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:26.738306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:28.163510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:29.348973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:30.701947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:32.029437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:33.312626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:34.903236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:36.200230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:21.629085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:23.143824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:24.370242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:25.609312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:26.845973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:28.261743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:29.447308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:30.808076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:32.124838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:33.725434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-08T11:11:35.004840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-12-08T11:11:42.297054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
num_refill_req_l3mtransport_issue_l1yCompetitor_in_mktretail_shop_numdistributor_numdist_from_hubworkers_numstorage_issue_reported_l3mwh_breakdown_l3mgovt_check_l3mproduct_wg_tondemand_interactionLocation_typeWH_capacity_sizezoneWH_regional_zonewh_owner_typeflood_impactedflood_proofelectric_supplytemp_reg_machapproved_wh_govt_certificate
num_refill_req_l3m1.0000.0160.001-0.0020.004-0.000-0.014-0.006-0.000-0.0030.0030.9490.0210.0080.0080.0080.0000.0120.0000.0180.3170.000
transport_issue_l1y0.0161.000-0.001-0.0030.0050.014-0.007-0.1430.0170.008-0.1690.0150.0060.0110.0060.0000.0000.0000.0000.0050.0260.011
Competitor_in_mkt0.001-0.0011.000-0.1800.0000.009-0.0010.0100.011-0.0560.010-0.0460.0000.0340.3080.0490.0000.0000.0000.0000.0140.000
retail_shop_num-0.002-0.003-0.1801.000-0.000-0.002-0.001-0.007-0.0110.065-0.0080.2580.0070.0590.0510.0450.0000.0000.0000.0000.0000.004
distributor_num0.0040.0050.000-0.0001.000-0.012-0.0120.0040.005-0.0070.0050.0040.0000.0030.0140.0000.0000.0050.0170.0090.0180.000
dist_from_hub-0.0000.0140.009-0.002-0.0121.000-0.021-0.005-0.000-0.001-0.0050.0010.0000.0000.0000.0000.0000.0180.0000.0000.0000.007
workers_num-0.014-0.007-0.001-0.001-0.012-0.0211.000-0.010-0.014-0.006-0.010-0.0150.0000.0000.0000.0060.2460.1710.0980.4000.0110.000
storage_issue_reported_l3m-0.006-0.1430.010-0.0070.004-0.005-0.0101.0000.350-0.0090.989-0.0060.0930.0110.0100.0080.0000.0000.0050.0170.1300.096
wh_breakdown_l3m-0.0000.0170.011-0.0110.005-0.000-0.0140.3501.000-0.0150.339-0.0040.0650.0000.0080.0000.0100.0000.0140.0000.1230.049
govt_check_l3m-0.0030.008-0.0560.065-0.007-0.001-0.006-0.009-0.0151.000-0.0090.0130.0100.6490.2840.5670.0100.0000.0050.0100.0000.000
product_wg_ton0.003-0.1690.010-0.0080.005-0.005-0.0100.9890.339-0.0091.0000.0030.0850.0140.0000.0040.0000.0000.0000.0130.1190.127
demand_interaction0.9490.015-0.0460.2580.0040.001-0.015-0.006-0.0040.0130.0031.0000.0210.0220.0180.0130.0050.0080.0000.0240.2890.007
Location_type0.0210.0060.0000.0070.0000.0000.0000.0930.0650.0100.0850.0211.0000.0110.0090.0100.0000.0000.0000.0000.0210.016
WH_capacity_size0.0080.0110.0340.0590.0030.0000.0000.0110.0000.6490.0140.0220.0111.0000.1740.8470.0000.0000.0000.0080.0000.000
zone0.0080.0060.3080.0510.0140.0000.0000.0100.0080.2840.0000.0180.0090.1741.0000.1780.0000.0110.0000.0000.0000.000
WH_regional_zone0.0080.0000.0490.0450.0000.0000.0060.0080.0000.5670.0040.0130.0100.8470.1781.0000.0000.0080.0060.0110.0120.003
wh_owner_type0.0000.0000.0000.0000.0000.0000.2460.0000.0100.0100.0000.0050.0000.0000.0000.0001.0000.1080.0290.2300.0000.000
flood_impacted0.0120.0000.0000.0000.0050.0180.1710.0000.0000.0000.0000.0080.0000.0000.0110.0080.1081.0000.1070.1650.0060.000
flood_proof0.0000.0000.0000.0000.0170.0000.0980.0050.0140.0050.0000.0000.0000.0000.0000.0060.0290.1071.0000.1140.0000.008
electric_supply0.0180.0050.0000.0000.0090.0000.4000.0170.0000.0100.0130.0240.0000.0080.0000.0110.2300.1650.1141.0000.0040.000
temp_reg_mach0.3170.0260.0140.0000.0180.0000.0110.1300.1230.0000.1190.2890.0210.0000.0000.0120.0000.0060.0000.0041.0000.445
approved_wh_govt_certificate0.0000.0110.0000.0040.0000.0070.0000.0960.0490.0000.1270.0070.0160.0000.0000.0030.0000.0000.0080.0000.4451.000

Missing values

2023-12-08T11:11:36.364922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-08T11:11:36.684899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-08T11:11:36.976517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Location_typeWH_capacity_sizezoneWH_regional_zonenum_refill_req_l3mtransport_issue_l1yCompetitor_in_mktretail_shop_numwh_owner_typedistributor_numflood_impactedflood_proofelectric_supplydist_from_hubworkers_numstorage_issue_reported_l3mtemp_reg_machapproved_wh_govt_certificatewh_breakdown_l3mgovt_check_l3mproduct_wg_tondemand_interaction
0UrbanSmallWestZone 63124651Rented240119129.0130A5151711513953
1RuralLargeNorthZone 50046217Company Owned4700121031.040A31750740
2RuralMidSouthZone 21044306Company Owned6400016137.0170A622231374306
3RuralMidNorthZone 37426000Rented5000010321.0171A+3272211542000
4RuralLargeNorthZone 53124740Company Owned4210111225.0180C6242407114220
5RuralSmallWestZone 18025053Rented3700115235.0231A+333213440424
6RuralLargeWestZone 68044449Company Owned380017727.0240B363014235592
7RuralLargeNorthZone 51047183Rented4500024123.0180C624240937183
8RuralSmallSouthZone 68145381Rented4200112422.0131A+521808243048
9RuralSmallSouthZone 64333869Company Owned350007843.060C62713015476
Location_typeWH_capacity_sizezoneWH_regional_zonenum_refill_req_l3mtransport_issue_l1yCompetitor_in_mktretail_shop_numwh_owner_typedistributor_numflood_impactedflood_proofelectric_supplydist_from_hubworkers_numstorage_issue_reported_l3mtemp_reg_machapproved_wh_govt_certificatewh_breakdown_l3mgovt_check_l3mproduct_wg_tondemand_interaction
24990RuralSmallSouthZone 13034124Rented190009827.0261A+3213409812372
24991RuralMidWestZone 44043672Rented5801018326.0281C6263706514688
24992RuralLargeWestZone 53024312Company Owned1600019020.0170A4102310112936
24993RuralMidSouthZone 35024591Rented3300116333.0220B+5192609122955
24994RuralMidNorthZone 47035242Rented410007125.090B1261108336694
24995RuralSmallNorthZone 13045390Rented1900114234.0221A2303209316170
24996RuralMidWestZone 26044490Company Owned5700113028.0100B4181211426940
24997UrbanLargeSouthZone 57025403Rented31101147NaN230B+5252708037821
24998RuralSmallNorthZone 110210562Rented250016025.0180A6302509310562
24999RuralMidWestZone 48245664Company Owned2101123939.040B+211505845312